Learning Non-linear Structures with Gaussian Markov Random Fields

AbstractNowadays, one of the most changeling points in statistics is the analysis of high dimensional data. In such cases, it is commonly assumed that the dimensionality of the data is only artificially high: although each data point is described by thousands of features, it is assumed that it can be modeled as a function of only a few underlying parameters. Formally, it is assumed that the data points are samples from a low-dimensional manifold embedded in a high-dimensional space.In this paper, we discuss a recently proposed method, known as Maximum Entropy Unfolding (MEU), for learning non-linear structures that characterize high dimensional data.This method represents a new perspective on spectral dimensionality reduction and, joined with the theory of Gaussian Markov random fields, provides a unifying probabilistic approach to spectral dimensionality reduction techniques. Parameter estimation as well as approaches to learning the structure of the GMRF are discusse

Simple Structure Detection Through Bayesian Exploratory Multidimensional IRT Models

In modern validity theory, a major concern is the construct validity of a test, which is commonly assessed through confirmatory or exploratory factor analysis. In the framework of Bayesian exploratory Multidimensional Item Response Theory (MIRT) models, we discuss two methods aimed at investigating the underlying structure of a test, in order to verify if the latent model adheres to a chosen simple factorial structure. This purpose is achieved without imposing hard constraints on the discrimination parameter matrix to address the rotational indeterminacy. The first approach prescribes a 2-step procedure. The parameter estimates are obtained through an unconstrained MCMC sampler. The simple structure is, then, inspected with a post-processing step based on the Consensus Simple Target Rotation technique. In the second approach, both rotational invariance and simple structure retrieval are addressed within the MCMC sampling scheme, by introducing a sparsity-inducing prior on the discrimination parameters. Through simulation as well as real-world studies, we demonstrate that the proposed methods are able to correctly infer the underlying sparse structure and to retrieve interpretable solutions

Order Selection of Spatial and Temporal Autoregressive Models with Errors in Variables

In this paper we consider the issues involved in model order selection for processes observed with additive Gaussian noise. In particular, we discuss conditional maximum likelihood estimation of noisy autoregressive models and provide an estimator that takes care of the observational noise. The estimator is weakly consistent, can be computed in only O(n) steps and can be used in the automatic model identification phase. Using information criteria, an extensive simulation study shows the results of order selection in the context of time and spatial series analysis

Thirty years of research into hate speech: topics of interest and their evolution

AbstractThe exponential growth of social media has brought with it an increasing propagation of hate speech and hate based propaganda. Hate speech is commonly defined as any communication that disparages a person or a group on the basis of some characteristics such as race, colour, ethnicity, gender, sexual orientation, nationality, religion. Online hate diffusion has now developed into a serious problem and this has led to a number of international initiatives being proposed, aimed at qualifying the problem and developing effective counter-measures. The aim of this paper is to analyse the knowledge structure of hate speech literature and the evolution of related topics. We apply co-word analysis methods to identify different topics treated in the field. The analysed database was downloaded from Scopus, focusing on a number of publications during the last thirty years. Topic and network analyses of literature showed that the main research topics can be divided into three areas: "general debate hate speech versus freedom of expression","hate-speech automatic detection and classification by machine-learning strategies", and "gendered hate speech and cyberbullying". The understanding of how research fronts interact led to stress the relevance of machine learning approaches to correctly assess hatred forms of online speech

La condizione detentiva, il trattamento e la relazione professionale con il detenuto autore di reati sessuali. Una visione esperienziale

L’articolo, partendo dalla presentazione di una innovativa esperienza trattamentale nei confronti di sex offender attuata nella Casa Circondariale di Chieti, vuole proporre l’importanza che i percorsi di inclusione nei confronti di tali autori di reato assumono quale presupposto indiscutibile per l’attuazione di programmi trattamentali specifici. L’esperienza svolta dagli operatori del carcere di Chieti si è avvalsa anche di una collaborazione con l’Università “G. d’Annunzio” che ha evidenziato come in tali autori di reato siano presenti significative distorsioni cognitive su cui è importante intervenire al fine di ottenere il recupero della persona e la riduzione della recidiva. Vengono esposti i risultati della ricerca svolta su 24 sex offender e non che ha evidenziato una presenza più significativa di distorsioni cognitive negli autori di reati sessuali rispetto agli autori di reato non a sfondo sessuale, soprattutto a danno di vittime maggiorenni piuttosto che minorenni. Vengono analizzati modelli trattamentali applicati a livello nazionale e internazionale e indicati successivi sviluppi di ricerca al fine di proporre programmi di intervento sulla stessa popolazione detenuta nel carcere di Chieti. Résumé À partir de la présentation d’un programme novateur axé sur la réinsertion de délinquants sexuels, mis en œuvre dans la prison italienne de Chieti (Casa Circondariale), l’article souligne l’importance de l’inclusion de ces délinquants en tant que condition essentielle à la réalisation de programmes de réinsertion spécifiques. Grâce à la collaboration entre le personnel pénitentiaire et les chercheurs de l’universitéUniversité « G. d’Annunzio », cette expérience prouve que ce type de délinquants est affecté par d’importants préjugés cognitifs. C’est pourquoi, il est important de réaliser une intervention de débiaisement afin d’une meilleure réhabilitation de la personne et pour contribuer à la réduction de la récidive. Les résultats de la recherche menée auprès de 24 personnes (dont certaines sont des délinquants sexuels, d’autres non) montrent que les délinquants sexuels sont affectés plus que les autres par des préjugés cognitifs et en particulier dans le cas où leurs victimes ont plus de 18 ans. En outre, dans cet article, les auteurs analysent les modèles italiens et internationaux de traitement et enfin proposent de nouvelles activités de recherche afin d’étendre ce type de programme à la totalité de la population carcérale de la prison de Chieti. Abstract Starting from the presentation of an innovative program addressing the rehabilitation of sex offenders, implemented inside the Italian prison of Chieti (Casa Circondariale), the article proposes the importance of the inclusion of these offenders as an essential condition for the implementation of specific rehabilitation programs. Thanks to the collaboration between the prison staff and the researchers coming from the University “G. d’Annunzio”, this experience shows that such offenders are affected by significant cognitive biases. Therefore, it is important to apply a debiasing intervention to better rehabilitate the person and contribute to the reduction of recidivism. The results of the research that was carried out on 24 people (some of them are sex offenders, some others not) show that the sex offenders are affected more than the others offenders by cognitive biases, and particularly when their victims were over 18. Moreover, in this paper the authors analyse Italian and international treatment models and finally they propose new research activities in order to extend this kind of program to the entire inmate population of the prison of Chieti

Framing automatic grading techniques for open-ended questionnaires responses. A short survey

The assessment of students' performances is one of the essential components of teaching activities, and it poses different challenges to teachers and instructors, especially when considering the grading of responses to open-ended questions (i.e., short-answers or essays). Open-ended tasks allow a more in-depth assessment of students' learning levels, but their evaluation and grading are time-consuming and prone to subjective bias. For these reasons, automatic grading techniques have been studied for a long time, focusing mainly on short-answers rather than long essays. Given the growing popularity of Massive Online Open Courses and the shifting from physical to virtual classrooms environments due to the Covid-19 pandemic, the adoption of questionnaires for evaluating learning performances has rapidly increased. Hence, it is of particular interest to analyze the recent effort of researchers in the development of techniques designed to grade students' responses to open-ended questions. In our work, we consider a systematic literature review focusing on automatic grading of open-ended written assignments. The study encompasses 488 articles published from 1984 to 2021 and aims at understanding the research trends and the techniques to tackle essay automatic grading. Lastly, inferences and recommendations are given for future works in the Learning Analytics field

Negative Affectivity Predicts Lower Quality of Life and Metabolic Control in Type 2 Diabetes Patients: A Structural Equation Modeling Approach

Introduction: It is essential to consider the clinical assessment of psychological aspects in patients with Diabetes Mellitus (DM), in order to prevent potentially adverse self-management care behaviors leading to diabetes-related complications, including declining levels of Quality of Life (QoL) and negative metabolic control.Purpose: In the framework of Structural Equation Modeling (SEM), the specific aim of this study is to evaluate the influence of distressed personality factors as Negative Affectivity (NA) and Social Inhibition (SI) on diabetes-related clinical variables (i.e., QoL and glycemic control).Methods: The total sample consists of a clinical sample, including 159 outpatients with Type 2 Diabetes Mellitus (T2DM), and a control group composed of 102 healthy respondents. All participants completed the following self- rating scales: The Type D Scale (DS14) and the World Health Organization QoL Scale (WHOQOLBREF). Furthermore, the participants of the clinical group were assessed for HbA1c, disease duration, and BMI. The observed covariates were BMI, gender, and disease duration, while HbA1c was considered an observed variable.Results: SEM analysis revealed significant differences between groups in regards to the latent construct of NA and the Environmental dimension of QoL. For the clinical sample, SEM showed that NA had a negative impact on both QoL dimensions and metabolic control.Conclusions: Clinical interventions aiming to improve medication adherence in patients with T2DM should include the psychological evaluation of Type D Personality traits, by focusing especially on its component of NA as a significant risk factor leading to negative health outcomes

The Offset Normal Shape Distribution for Dynamic Shape Analysis

This paper deals with the statistical analysis of landmark data observed at different temporal instants. Statistical analysis of dynamic shapes is a problem with significant challenges due to the difficulty in providing a description of the shape changes over time, across subjects and over groups of subjects. There are several modelling strategies which can be used for dynamic shape analysis. Here, we use the exact distribution theory for the shape of planar correlated Gaussian configurations and derive the induced offset-normal shape distribution. Various properties of this distribution are investigated, and some special cases discussed. This work is a natural progression of what has been proposed in Mardia and Dryden (1989), Dryden and Mardia (1991), Mardia andWalder (1994) and Kume and Welling (2010)